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This article mainly introduces how jupyterhub uses conda, pip or apt to install dependent libraries, which has a certain reference value, and interested friends can refer to it. I hope you can learn a lot after reading this article.

In TLJH, all users use the same conda environment, users can call all the software packages in the environment, and administrators can install the required software environment for users in a variety of ways.

Install dependency packages through pip

The pip tool is used to download installation dependencies from Python Packaging Index (PyPI), and there are more than 145000 software packages in PyPI.

1. Log in to JupyterHub with administrator privileges and open a command line window

2. Enter the installation command sudo-E pip install

Replace the angle brackets in the command as needed, taking installing numpy as an example:

Install dependency packages through conda

Conda can install packages from other languages, such as node,R, etc., especially when dependent packages need to be linked to C/Fortran code, it is recommended to use conda instead of pip installation

1. Log in to JupyterHub with administrator privileges and open a command line window 2. Enter the installation command sudo-E conda install-c conda-forge

Replace the angle brackets in the command as needed, taking installing gdal as an example:

Install dependency packages through apt

Apt is the official package manager for Ubuntu Linux distributions. It can install utilities (such as vim, sl, htop, etc.), server applications (postgres, mysql, nginx, etc.), support more languages than conda (haskell, prolog, INTERCAL), and some third-party software (such as RStudio). Apt supports offline installation using .deb files.

1. Log in to JupyterHub with administrator privileges and open a command line window 2. Update the list of available packages, make sure to get the latest version of sudo apt update3, and enter the installation command sudo apt install

Take installing git as an example:

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